Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3521–3530 of 6661 papers

Title	Date	Tasks	Status
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning	Feb 14, 2022	Contrastive LearningFederated Learning	—Unverified
Federated Contrastive Learning for Personalized Semantic Communication	Jun 13, 2024	Contrastive LearningDecoder	—Unverified
Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation	Sep 29, 2021	Contrastive LearningImage-to-Image Translation	—Unverified
Federated Contrastive Learning for Volumetric Medical Image Segmentation	Apr 23, 2022	Contrastive LearningFederated Learning	—Unverified
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading	Feb 13, 2023	Contrastive LearningEthics	—Unverified
Federated Contrastive Learning of Graph-Level Representations	Nov 18, 2024	ClusteringContrastive Learning	—Unverified
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching	Sep 29, 2021	Contrastive LearningFederated Learning	—Unverified
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation	Mar 21, 2025	Click-Through Rate PredictionContrastive Learning	—Unverified
Federated Generalized Category Discovery	May 23, 2023	Contrastive Learning	—Unverified
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning	Jul 24, 2022	Contrastive LearningFederated Learning	—Unverified

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified